Overview

Dataset statistics

Number of variables14
Number of observations17947
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory2.6 MiB
Average record size in memory149.4 B

Variable types

Categorical4
Numeric9
Boolean1

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
name has a high cardinality: 17356 distinct valuesHigh cardinality
host_name has a high cardinality: 2739 distinct valuesHigh cardinality
neighbourhood has a high cardinality: 52 distinct valuesHigh cardinality
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 1 other fieldsHigh correlation
neighbourhood is highly overall correlated with latitude and 1 other fieldsHigh correlation
room_type is highly imbalanced (67.5%)Imbalance
price is highly skewed (γ1 = 85.44200033)Skewed
name is uniformly distributedUniform
number_of_reviews has 3807 (21.2%) zerosZeros
availability_365 has 1107 (6.2%) zerosZeros

Reproduction

Analysis started2023-01-18 16:11:13.923613
Analysis finished2023-01-18 16:11:22.760191
Duration8.84 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct17356
Distinct (%)96.7%
Missing3
Missing (%)< 0.1%
Memory size796.5 KiB
★★★★★ Brand New Remodeled Apt in Barrio Norte
 
18
Hermoso departamento en Palermo
 
15
Práctico estudio en Monserrat
 
13
Increíble casa a metros del obelisco! Palacio Lima
 
13
Fantastic room in the heart of Buenos Aires
 
13
Other values (17351)
17872 

Length

Max length244
Median length99
Mean length39.328801
Min length1

Characters and Unicode

Total characters705716
Distinct characters282
Distinct categories21 ?
Distinct scripts8 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17047 ?
Unique (%)95.0%

Sample

1st rowAmazing Luxurious Apt-Palermo Soho
2nd rowRELAX IN HAPPY HOUSE - PALERMO, BUENOS AIRES
3rd rowEntire Studio/apt in Buenos Aires
4th rowSPECTACULAR ANCIENT HOUSE
5th rowGreat apt 1 Bedroom - 1.5 Bath /Recoleta

Common Values

ValueCountFrequency (%)
★★★★★ Brand New Remodeled Apt in Barrio Norte 18
 
0.1%
Hermoso departamento en Palermo 15
 
0.1%
Práctico estudio en Monserrat 13
 
0.1%
Increíble casa a metros del obelisco! Palacio Lima 13
 
0.1%
Fantastic room in the heart of Buenos Aires 13
 
0.1%
★★★★★Luxury Boutique Suite in Palermo Hollywood 11
 
0.1%
Hermoso departamento en Palermo Hollywood 11
 
0.1%
★★★★★Boutique Studio Suite in Palermo Hollywood 10
 
0.1%
Modern&Luxury Aparts In Recoleta 10
 
0.1%
One Bedroom Apartment - PALERMO HOLLYWOOD 9
 
0.1%
Other values (17346) 17821
99.3%

Length

2023-01-18T17:11:22.870091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 4875
 
4.4%
palermo 4804
 
4.3%
in 3949
 
3.5%
3366
 
3.0%
departamento 2832
 
2.5%
apartment 2200
 
2.0%
recoleta 2087
 
1.9%
studio 1905
 
1.7%
y 1871
 
1.7%
de 1825
 
1.6%
Other values (6491) 81536
73.3%

Most occurring characters

ValueCountFrequency (%)
94352
 
13.4%
e 61550
 
8.7%
o 57868
 
8.2%
a 52858
 
7.5%
n 38350
 
5.4%
t 38121
 
5.4%
r 35462
 
5.0%
i 32172
 
4.6%
l 27217
 
3.9%
m 22861
 
3.2%
Other values (272) 244905
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 494264
70.0%
Space Separator 94356
 
13.4%
Uppercase Letter 92981
 
13.2%
Other Punctuation 12223
 
1.7%
Decimal Number 6637
 
0.9%
Dash Punctuation 2321
 
0.3%
Other Symbol 1130
 
0.2%
Math Symbol 621
 
0.1%
Close Punctuation 396
 
0.1%
Open Punctuation 370
 
0.1%
Other values (11) 417
 
0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
720
63.7%
78
 
6.9%
51
 
4.5%
29
 
2.6%
24
 
2.1%
° 20
 
1.8%
20
 
1.8%
14
 
1.2%
10
 
0.9%
9
 
0.8%
Other values (77) 155
 
13.7%
Lowercase Letter
ValueCountFrequency (%)
e 61550
12.5%
o 57868
11.7%
a 52858
10.7%
n 38350
 
7.8%
t 38121
 
7.7%
r 35462
 
7.2%
i 32172
 
6.5%
l 27217
 
5.5%
m 22861
 
4.6%
s 18666
 
3.8%
Other values (48) 109139
22.1%
Other Letter
ValueCountFrequency (%)
º 22
30.1%
ה 4
 
5.5%
ב 4
 
5.5%
3
 
4.1%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
Other values (27) 28
38.4%
Uppercase Letter
ValueCountFrequency (%)
A 10022
 
10.8%
P 8741
 
9.4%
S 6769
 
7.3%
B 6504
 
7.0%
E 6109
 
6.6%
R 6044
 
6.5%
L 5467
 
5.9%
C 5454
 
5.9%
O 5035
 
5.4%
T 4521
 
4.9%
Other values (23) 28315
30.5%
Other Punctuation
ValueCountFrequency (%)
. 3438
28.1%
, 3021
24.7%
! 2400
19.6%
/ 1395
11.4%
& 941
 
7.7%
: 192
 
1.6%
" 172
 
1.4%
@ 146
 
1.2%
' 142
 
1.2%
* 127
 
1.0%
Other values (9) 249
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 2225
33.5%
1 1134
17.1%
3 821
 
12.4%
4 676
 
10.2%
0 644
 
9.7%
5 430
 
6.5%
6 249
 
3.8%
7 180
 
2.7%
8 143
 
2.2%
9 135
 
2.0%
Math Symbol
ValueCountFrequency (%)
+ 386
62.2%
| 204
32.9%
= 14
 
2.3%
> 9
 
1.4%
< 4
 
0.6%
~ 3
 
0.5%
1
 
0.2%
Modifier Symbol
ValueCountFrequency (%)
´ 31
72.1%
^ 4
 
9.3%
🏼 3
 
7.0%
¨ 2
 
4.7%
` 1
 
2.3%
🏾 1
 
2.3%
🏻 1
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 2307
99.4%
12
 
0.5%
2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 389
98.2%
4
 
1.0%
] 3
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 364
98.4%
[ 3
 
0.8%
3
 
0.8%
Space Separator
ValueCountFrequency (%)
94352
> 99.9%
  4
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
24
52.2%
22
47.8%
Initial Punctuation
ValueCountFrequency (%)
18
78.3%
5
 
21.7%
Format
ValueCountFrequency (%)
10
66.7%
5
33.3%
Other Number
ValueCountFrequency (%)
² 8
88.9%
½ 1
 
11.1%
Control
ValueCountFrequency (%)
100
100.0%
Nonspacing Mark
ValueCountFrequency (%)
76
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 17
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%
Modifier Letter
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 587267
83.2%
Common 118317
 
16.8%
Inherited 81
 
< 0.1%
Katakana 22
 
< 0.1%
Han 13
 
< 0.1%
Hebrew 8
 
< 0.1%
Hiragana 7
 
< 0.1%
Hangul 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
94352
79.7%
. 3438
 
2.9%
, 3021
 
2.6%
! 2400
 
2.0%
- 2307
 
1.9%
2 2225
 
1.9%
/ 1395
 
1.2%
1 1134
 
1.0%
& 941
 
0.8%
3 821
 
0.7%
Other values (142) 6283
 
5.3%
Latin
ValueCountFrequency (%)
e 61550
 
10.5%
o 57868
 
9.9%
a 52858
 
9.0%
n 38350
 
6.5%
t 38121
 
6.5%
r 35462
 
6.0%
i 32172
 
5.5%
l 27217
 
4.6%
m 22861
 
3.9%
s 18666
 
3.2%
Other values (82) 202142
34.4%
Katakana
ValueCountFrequency (%)
3
13.6%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (6) 6
27.3%
Han
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
Hiragana
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Inherited
ValueCountFrequency (%)
76
93.8%
5
 
6.2%
Hebrew
ValueCountFrequency (%)
ה 4
50.0%
ב 4
50.0%
Hangul
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 699747
99.2%
None 4695
 
0.7%
Misc Symbols 825
 
0.1%
Punctuation 145
 
< 0.1%
Dingbats 128
 
< 0.1%
VS 76
 
< 0.1%
Phonetic Ext 25
 
< 0.1%
Katakana 24
 
< 0.1%
CJK 13
 
< 0.1%
Hebrew 8
 
< 0.1%
Other values (7) 30
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
94352
 
13.5%
e 61550
 
8.8%
o 57868
 
8.3%
a 52858
 
7.6%
n 38350
 
5.5%
t 38121
 
5.4%
r 35462
 
5.1%
i 32172
 
4.6%
l 27217
 
3.9%
m 22861
 
3.3%
Other values (83) 238936
34.1%
None
ValueCountFrequency (%)
ó 2177
46.4%
ñ 855
 
18.2%
í 392
 
8.3%
á 381
 
8.1%
ú 168
 
3.6%
é 146
 
3.1%
Ó 67
 
1.4%
Ñ 65
 
1.4%
51
 
1.1%
¡ 36
 
0.8%
Other values (80) 357
 
7.6%
Misc Symbols
ValueCountFrequency (%)
720
87.3%
29
 
3.5%
24
 
2.9%
20
 
2.4%
10
 
1.2%
7
 
0.8%
4
 
0.5%
3
 
0.4%
2
 
0.2%
2
 
0.2%
Other values (4) 4
 
0.5%
Dingbats
ValueCountFrequency (%)
78
60.9%
14
 
10.9%
9
 
7.0%
6
 
4.7%
4
 
3.1%
3
 
2.3%
3
 
2.3%
2
 
1.6%
2
 
1.6%
2
 
1.6%
Other values (5) 5
 
3.9%
VS
ValueCountFrequency (%)
76
100.0%
Punctuation
ValueCountFrequency (%)
45
31.0%
24
16.6%
22
15.2%
18
 
12.4%
12
 
8.3%
10
 
6.9%
5
 
3.4%
5
 
3.4%
2
 
1.4%
2
 
1.4%
Phonetic Ext
ValueCountFrequency (%)
6
24.0%
5
20.0%
4
16.0%
3
12.0%
3
12.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
Hebrew
ValueCountFrequency (%)
ה 4
50.0%
ב 4
50.0%
IPA Ext
ValueCountFrequency (%)
ɴ 3
37.5%
ʀ 2
25.0%
ʏ 1
 
12.5%
ʟ 1
 
12.5%
ɢ 1
 
12.5%
Katakana
ValueCountFrequency (%)
3
12.5%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (7) 7
29.2%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇦 3
50.0%
🇷 3
50.0%
CJK
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
Hiragana
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Geometric Shapes
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Emoticons
ValueCountFrequency (%)
😊 1
33.3%
😍 1
33.3%
😘 1
33.3%
Box Drawing
ValueCountFrequency (%)
1
100.0%
Hangul
ValueCountFrequency (%)
1
100.0%

host_id
Real number (ℝ)

Distinct10127
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.269011 × 108
Minimum13426
Maximum4.8035407 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.5 KiB
2023-01-18T17:11:22.977768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum13426
5-th percentile1028764
Q113163492
median70427733
Q32.1014706 × 108
95-th percentile4.4224643 × 108
Maximum4.8035407 × 108
Range4.8034064 × 108
Interquartile range (IQR)1.9698356 × 108

Descriptive statistics

Standard deviation1.3724907 × 108
Coefficient of variation (CV)1.0815436
Kurtosis0.13537264
Mean1.269011 × 108
Median Absolute Deviation (MAD)65992062
Skewness1.1041507
Sum2.2774941 × 1012
Variance1.8837308 × 1016
MonotonicityNot monotonic
2023-01-18T17:11:23.077296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3469227 132
 
0.7%
55553719 112
 
0.6%
7786587 106
 
0.6%
1021694 102
 
0.6%
278440549 93
 
0.5%
1028764 87
 
0.5%
1875949 75
 
0.4%
132838002 74
 
0.4%
10966956 70
 
0.4%
210147055 68
 
0.4%
Other values (10117) 17028
94.9%
ValueCountFrequency (%)
13426 14
0.1%
20848 2
 
< 0.1%
22670 2
 
< 0.1%
32926 2
 
< 0.1%
36390 1
 
< 0.1%
36441 2
 
< 0.1%
42762 1
 
< 0.1%
52100 1
 
< 0.1%
54480 1
 
< 0.1%
55825 1
 
< 0.1%
ValueCountFrequency (%)
480354068 1
< 0.1%
480353632 1
< 0.1%
480315953 1
< 0.1%
480311197 1
< 0.1%
480206190 1
< 0.1%
480160752 1
< 0.1%
480159803 1
< 0.1%
480067053 1
< 0.1%
480048451 1
< 0.1%
480028629 1
< 0.1%

host_name
Categorical

Distinct2739
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size796.5 KiB
Federico
 
320
Pablo
 
279
Diego
 
215
Maria
 
201
Juan
 
188
Other values (2734)
16744 

Length

Max length34
Median length28
Mean length7.5186382
Min length1

Characters and Unicode

Total characters134937
Distinct characters88
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1493 ?
Unique (%)8.3%

Sample

1st rowCandela
2nd rowMaría
3rd rowRoxana
4th rowCintia
5th rowLuciano

Common Values

ValueCountFrequency (%)
Federico 320
 
1.8%
Pablo 279
 
1.6%
Diego 215
 
1.2%
Maria 201
 
1.1%
Juan 188
 
1.0%
Cecilia 179
 
1.0%
Martin 167
 
0.9%
Mariana 141
 
0.8%
Maximiliano 141
 
0.8%
Gabriel 137
 
0.8%
Other values (2729) 15979
89.0%

Length

2023-01-18T17:11:23.177307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maria 540
 
2.5%
juan 402
 
1.8%
pablo 352
 
1.6%
federico 325
 
1.5%
295
 
1.3%
y 295
 
1.3%
diego 240
 
1.1%
martin 208
 
1.0%
cecilia 204
 
0.9%
fernando 199
 
0.9%
Other values (1896) 18809
86.0%

Most occurring characters

ValueCountFrequency (%)
a 21411
15.9%
i 13116
 
9.7%
e 9724
 
7.2%
n 9529
 
7.1%
r 9470
 
7.0%
o 8912
 
6.6%
l 7893
 
5.8%
t 3997
 
3.0%
u 3963
 
2.9%
3927
 
2.9%
Other values (78) 42995
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 108439
80.4%
Uppercase Letter 22006
 
16.3%
Space Separator 3927
 
2.9%
Other Punctuation 404
 
0.3%
Decimal Number 104
 
0.1%
Dash Punctuation 17
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Connector Punctuation 8
 
< 0.1%
Other Symbol 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 21411
19.7%
i 13116
12.1%
e 9724
9.0%
n 9529
8.8%
r 9470
8.7%
o 8912
8.2%
l 7893
 
7.3%
t 3997
 
3.7%
u 3963
 
3.7%
s 3924
 
3.6%
Other values (30) 16500
15.2%
Uppercase Letter
ValueCountFrequency (%)
M 3403
15.5%
A 2294
 
10.4%
C 1673
 
7.6%
J 1452
 
6.6%
F 1447
 
6.6%
S 1324
 
6.0%
L 1321
 
6.0%
G 1188
 
5.4%
P 982
 
4.5%
D 938
 
4.3%
Other values (18) 5984
27.2%
Decimal Number
ValueCountFrequency (%)
2 74
71.2%
7 17
 
16.3%
1 5
 
4.8%
6 3
 
2.9%
5 3
 
2.9%
3 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
& 352
87.1%
. 37
 
9.2%
/ 8
 
2.0%
, 6
 
1.5%
@ 1
 
0.2%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Space Separator
ValueCountFrequency (%)
3927
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Other Symbol
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130445
96.7%
Common 4489
 
3.3%
Hangul 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 21411
16.4%
i 13116
 
10.1%
e 9724
 
7.5%
n 9529
 
7.3%
r 9470
 
7.3%
o 8912
 
6.8%
l 7893
 
6.1%
t 3997
 
3.1%
u 3963
 
3.0%
s 3924
 
3.0%
Other values (58) 38506
29.5%
Common
ValueCountFrequency (%)
3927
87.5%
& 352
 
7.8%
2 74
 
1.6%
. 37
 
0.8%
- 17
 
0.4%
7 17
 
0.4%
( 12
 
0.3%
) 12
 
0.3%
/ 8
 
0.2%
_ 8
 
0.2%
Other values (7) 25
 
0.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134101
99.4%
None 828
 
0.6%
Misc Symbols 5
 
< 0.1%
Hangul 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 21411
16.0%
i 13116
 
9.8%
e 9724
 
7.3%
n 9529
 
7.1%
r 9470
 
7.1%
o 8912
 
6.6%
l 7893
 
5.9%
t 3997
 
3.0%
u 3963
 
3.0%
3927
 
2.9%
Other values (58) 42159
31.4%
None
ValueCountFrequency (%)
í 324
39.1%
á 212
25.6%
é 168
20.3%
ó 82
 
9.9%
ú 10
 
1.2%
ñ 8
 
1.0%
É 6
 
0.7%
Á 5
 
0.6%
ê 4
 
0.5%
ì 2
 
0.2%
Other values (6) 7
 
0.8%
Misc Symbols
ValueCountFrequency (%)
5
100.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

neighbourhood
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size796.5 KiB
Palermo
6085 
Recoleta
2758 
San Nicolas
1066 
Retiro
880 
Belgrano
871 
Other values (47)
6287 

Length

Max length17
Median length16
Mean length8.2162478
Min length4

Characters and Unicode

Total characters147457
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPalermo
2nd rowPalermo
3rd rowPalermo
4th rowSan Nicolas
5th rowRecoleta

Common Values

ValueCountFrequency (%)
Palermo 6085
33.9%
Recoleta 2758
15.4%
San Nicolas 1066
 
5.9%
Retiro 880
 
4.9%
Belgrano 871
 
4.9%
Monserrat 650
 
3.6%
Almagro 644
 
3.6%
Balvanera 589
 
3.3%
Villa Crespo 555
 
3.1%
Nuñez 503
 
2.8%
Other values (42) 3346
18.6%

Length

2023-01-18T17:11:23.268147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
palermo 6085
28.8%
recoleta 2758
13.1%
san 1645
 
7.8%
nicolas 1066
 
5.0%
villa 1036
 
4.9%
retiro 880
 
4.2%
belgrano 871
 
4.1%
monserrat 650
 
3.1%
almagro 644
 
3.1%
balvanera 589
 
2.8%
Other values (52) 4889
23.2%

Most occurring characters

ValueCountFrequency (%)
a 20604
14.0%
e 18260
12.4%
l 16408
11.1%
o 16392
11.1%
r 13010
 
8.8%
m 7248
 
4.9%
P 6577
 
4.5%
t 5938
 
4.0%
i 4905
 
3.3%
c 4622
 
3.1%
Other values (35) 33493
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123164
83.5%
Uppercase Letter 21074
 
14.3%
Space Separator 3166
 
2.1%
Decimal Number 39
 
< 0.1%
Other Punctuation 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20604
16.7%
e 18260
14.8%
l 16408
13.3%
o 16392
13.3%
r 13010
10.6%
m 7248
 
5.9%
t 5938
 
4.8%
i 4905
 
4.0%
c 4622
 
3.8%
n 4401
 
3.6%
Other values (13) 11376
9.2%
Uppercase Letter
ValueCountFrequency (%)
P 6577
31.2%
R 3676
17.4%
C 1968
 
9.3%
S 1871
 
8.9%
B 1728
 
8.2%
N 1581
 
7.5%
V 1055
 
5.0%
M 909
 
4.3%
A 668
 
3.2%
T 490
 
2.3%
Other values (6) 551
 
2.6%
Decimal Number
ValueCountFrequency (%)
2 20
51.3%
4 7
 
17.9%
3 6
 
15.4%
1 6
 
15.4%
Space Separator
ValueCountFrequency (%)
3166
100.0%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 144238
97.8%
Common 3219
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20604
14.3%
e 18260
12.7%
l 16408
11.4%
o 16392
11.4%
r 13010
9.0%
m 7248
 
5.0%
P 6577
 
4.6%
t 5938
 
4.1%
i 4905
 
3.4%
c 4622
 
3.2%
Other values (29) 30274
21.0%
Common
ValueCountFrequency (%)
3166
98.4%
2 20
 
0.6%
. 14
 
0.4%
4 7
 
0.2%
3 6
 
0.2%
1 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146954
99.7%
None 503
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20604
14.0%
e 18260
12.4%
l 16408
11.2%
o 16392
11.2%
r 13010
 
8.9%
m 7248
 
4.9%
P 6577
 
4.5%
t 5938
 
4.0%
i 4905
 
3.3%
c 4622
 
3.1%
Other values (34) 32990
22.4%
None
ValueCountFrequency (%)
ñ 503
100.0%

latitude
Real number (ℝ)

Distinct9291
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-34.59046
Minimum-34.6937
Maximum-34.51399
Zeros0
Zeros (%)0.0%
Negative17947
Negative (%)100.0%
Memory size796.5 KiB
2023-01-18T17:11:23.360693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-34.6937
5-th percentile-34.62141
Q1-34.601055
median-34.59003
Q3-34.58043
95-th percentile-34.555868
Maximum-34.51399
Range0.17971
Interquartile range (IQR)0.020625

Descriptive statistics

Standard deviation0.019324839
Coefficient of variation (CV)-0.00055867538
Kurtosis1.3672703
Mean-34.59046
Median Absolute Deviation (MAD)0.01017
Skewness0.15345182
Sum-620794.99
Variance0.00037344939
MonotonicityNot monotonic
2023-01-18T17:11:23.457295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-34.59487 49
 
0.3%
-34.584511 35
 
0.2%
-34.5887845 19
 
0.1%
-34.57682 17
 
0.1%
-34.60621 14
 
0.1%
-34.59652 13
 
0.1%
-34.57785 13
 
0.1%
-34.61376 12
 
0.1%
-34.60252 11
 
0.1%
-34.58833 11
 
0.1%
Other values (9281) 17753
98.9%
ValueCountFrequency (%)
-34.6937 1
< 0.1%
-34.68962 1
< 0.1%
-34.68911 1
< 0.1%
-34.68885 1
< 0.1%
-34.68388 1
< 0.1%
-34.68112 1
< 0.1%
-34.68076 1
< 0.1%
-34.6802 1
< 0.1%
-34.6792296 1
< 0.1%
-34.67749 1
< 0.1%
ValueCountFrequency (%)
-34.51399 1
< 0.1%
-34.51467 1
< 0.1%
-34.5163 1
< 0.1%
-34.51637 1
< 0.1%
-34.5164 1
< 0.1%
-34.51692 1
< 0.1%
-34.51711 1
< 0.1%
-34.5173 1
< 0.1%
-34.51733 1
< 0.1%
-34.51746 1
< 0.1%

longitude
Real number (ℝ)

Distinct11044
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-58.418014
Minimum-58.54437
Maximum-58.35541
Zeros0
Zeros (%)0.0%
Negative17947
Negative (%)100.0%
Memory size796.5 KiB
2023-01-18T17:11:23.560887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-58.54437
5-th percentile-58.471411
Q1-58.437345
median-58.41955
Q3-58.393675
95-th percentile-58.37333
Maximum-58.35541
Range0.18896
Interquartile range (IQR)0.043670141

Descriptive statistics

Standard deviation0.030585355
Coefficient of variation (CV)-0.00052356034
Kurtosis0.002658057
Mean-58.418014
Median Absolute Deviation (MAD)0.02123
Skewness-0.40926698
Sum-1048428.1
Variance0.00093546394
MonotonicityNot monotonic
2023-01-18T17:11:23.662945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-58.39603 43
 
0.2%
-58.437511 35
 
0.2%
-58.4138297 19
 
0.1%
-58.42192 18
 
0.1%
-58.39078 17
 
0.1%
-58.43845 16
 
0.1%
-58.42381 15
 
0.1%
-58.38242 12
 
0.1%
-58.37945 12
 
0.1%
-58.430573 11
 
0.1%
Other values (11034) 17749
98.9%
ValueCountFrequency (%)
-58.54437 1
< 0.1%
-58.54404 1
< 0.1%
-58.54275 1
< 0.1%
-58.54105 1
< 0.1%
-58.54078 1
< 0.1%
-58.53868 1
< 0.1%
-58.53667 1
< 0.1%
-58.53625 2
< 0.1%
-58.53614 1
< 0.1%
-58.534527 1
< 0.1%
ValueCountFrequency (%)
-58.35541 1
< 0.1%
-58.35553 1
< 0.1%
-58.35662 1
< 0.1%
-58.35767 1
< 0.1%
-58.35777 1
< 0.1%
-58.35887 1
< 0.1%
-58.35906 1
< 0.1%
-58.35908 1
< 0.1%
-58.35933 1
< 0.1%
-58.35943 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size796.5 KiB
Entire home/apt
15603 
Private room
2030 
Shared room
 
194
Hotel room
 
120

Length

Max length15
Median length15
Mean length14.583997
Min length10

Characters and Unicode

Total characters261739
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 15603
86.9%
Private room 2030
 
11.3%
Shared room 194
 
1.1%
Hotel room 120
 
0.7%

Length

2023-01-18T17:11:23.767128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T17:11:23.868163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 15603
43.5%
home/apt 15603
43.5%
room 2344
 
6.5%
private 2030
 
5.7%
shared 194
 
0.5%
hotel 120
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 33550
12.8%
t 33356
12.7%
o 20411
 
7.8%
r 20171
 
7.7%
m 17947
 
6.9%
17947
 
6.9%
a 17827
 
6.8%
i 17633
 
6.7%
h 15797
 
6.0%
p 15603
 
6.0%
Other values (9) 51497
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210242
80.3%
Space Separator 17947
 
6.9%
Uppercase Letter 17947
 
6.9%
Other Punctuation 15603
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33550
16.0%
t 33356
15.9%
o 20411
9.7%
r 20171
9.6%
m 17947
8.5%
a 17827
8.5%
i 17633
8.4%
h 15797
7.5%
p 15603
7.4%
n 15603
7.4%
Other values (3) 2344
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
E 15603
86.9%
P 2030
 
11.3%
S 194
 
1.1%
H 120
 
0.7%
Space Separator
ValueCountFrequency (%)
17947
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 15603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 228189
87.2%
Common 33550
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33550
14.7%
t 33356
14.6%
o 20411
8.9%
r 20171
8.8%
m 17947
7.9%
a 17827
7.8%
i 17633
7.7%
h 15797
6.9%
p 15603
6.8%
E 15603
6.8%
Other values (7) 20291
8.9%
Common
ValueCountFrequency (%)
17947
53.5%
/ 15603
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 261739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 33550
12.8%
t 33356
12.7%
o 20411
 
7.8%
r 20171
 
7.7%
m 17947
 
6.9%
17947
 
6.9%
a 17827
 
6.8%
i 17633
 
6.7%
h 15797
 
6.0%
p 15603
 
6.0%
Other values (9) 51497
19.7%

price
Real number (ℝ)

Distinct2291
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11307.989
Minimum260
Maximum14330511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.5 KiB
2023-01-18T17:11:24.140189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum260
5-th percentile2171
Q14136
median5790
Q38685
95-th percentile20265
Maximum14330511
Range14330251
Interquartile range (IQR)4549

Descriptive statistics

Standard deviation154991.57
Coefficient of variation (CV)13.706379
Kurtosis7761.1374
Mean11307.989
Median Absolute Deviation (MAD)2171
Skewness85.442
Sum2.0294447 × 108
Variance2.4022388 × 1010
MonotonicityNot monotonic
2023-01-18T17:11:24.241485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5066 636
 
3.5%
5790 630
 
3.5%
4343 619
 
3.4%
7238 557
 
3.1%
6514 468
 
2.6%
3619 443
 
2.5%
8685 355
 
2.0%
2895 351
 
2.0%
4053 268
 
1.5%
10133 246
 
1.4%
Other values (2281) 13374
74.5%
ValueCountFrequency (%)
260 1
< 0.1%
400 1
< 0.1%
471 1
< 0.1%
500 2
< 0.1%
598 1
< 0.1%
600 2
< 0.1%
650 2
< 0.1%
655 1
< 0.1%
680 1
< 0.1%
685 1
< 0.1%
ValueCountFrequency (%)
14330511 1
 
< 0.1%
14000000 1
 
< 0.1%
1447526 2
 
< 0.1%
1447382 8
< 0.1%
1158021 1
 
< 0.1%
889525 1
 
< 0.1%
733689 1
 
< 0.1%
727857 1
 
< 0.1%
723763 2
 
< 0.1%
694813 1
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6823982
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.5 KiB
2023-01-18T17:11:24.347271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile30
Maximum1000
Range999
Interquartile range (IQR)3

Descriptive statistics

Standard deviation27.965044
Coefficient of variation (CV)3.6401451
Kurtosis482.61596
Mean7.6823982
Median Absolute Deviation (MAD)2
Skewness17.928538
Sum137876
Variance782.04367
MonotonicityNot monotonic
2023-01-18T17:11:24.449081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3844
21.4%
2 3699
20.6%
1 3597
20.0%
7 1443
 
8.0%
4 1399
 
7.8%
5 1289
 
7.2%
30 543
 
3.0%
6 344
 
1.9%
15 330
 
1.8%
10 295
 
1.6%
Other values (61) 1164
 
6.5%
ValueCountFrequency (%)
1 3597
20.0%
2 3699
20.6%
3 3844
21.4%
4 1399
 
7.8%
5 1289
 
7.2%
6 344
 
1.9%
7 1443
 
8.0%
8 34
 
0.2%
9 13
 
0.1%
10 295
 
1.6%
ValueCountFrequency (%)
1000 2
 
< 0.1%
999 2
 
< 0.1%
750 1
 
< 0.1%
730 2
 
< 0.1%
500 1
 
< 0.1%
365 18
0.1%
364 1
 
< 0.1%
360 4
 
< 0.1%
359 4
 
< 0.1%
350 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

Distinct284
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.9888
Minimum0
Maximum577
Zeros3807
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size796.5 KiB
2023-01-18T17:11:24.544592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q324
95-th percentile94
Maximum577
Range577
Interquartile range (IQR)23

Descriptive statistics

Standard deviation37.816027
Coefficient of variation (CV)1.8017241
Kurtosis21.431272
Mean20.9888
Median Absolute Deviation (MAD)6
Skewness3.8147239
Sum376686
Variance1430.0519
MonotonicityNot monotonic
2023-01-18T17:11:24.635464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3807
21.2%
1 1543
 
8.6%
2 1032
 
5.8%
3 789
 
4.4%
4 703
 
3.9%
5 571
 
3.2%
6 533
 
3.0%
7 472
 
2.6%
8 420
 
2.3%
9 387
 
2.2%
Other values (274) 7690
42.8%
ValueCountFrequency (%)
0 3807
21.2%
1 1543
8.6%
2 1032
 
5.8%
3 789
 
4.4%
4 703
 
3.9%
5 571
 
3.2%
6 533
 
3.0%
7 472
 
2.6%
8 420
 
2.3%
9 387
 
2.2%
ValueCountFrequency (%)
577 1
< 0.1%
519 1
< 0.1%
458 1
< 0.1%
453 1
< 0.1%
373 1
< 0.1%
366 1
< 0.1%
363 1
< 0.1%
362 1
< 0.1%
354 1
< 0.1%
345 2
< 0.1%
Distinct55
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.396055
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.5 KiB
2023-01-18T17:11:24.735680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile68
Maximum132
Range131
Interquartile range (IQR)7

Descriptive statistics

Standard deviation23.298279
Coefficient of variation (CV)2.0444162
Kurtosis9.8332417
Mean11.396055
Median Absolute Deviation (MAD)1
Skewness3.1071211
Sum204525
Variance542.80981
MonotonicityNot monotonic
2023-01-18T17:11:24.838131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7971
44.4%
2 2288
 
12.7%
3 1161
 
6.5%
4 776
 
4.3%
5 515
 
2.9%
8 344
 
1.9%
6 336
 
1.9%
7 294
 
1.6%
9 207
 
1.2%
12 192
 
1.1%
Other values (45) 3863
21.5%
ValueCountFrequency (%)
1 7971
44.4%
2 2288
 
12.7%
3 1161
 
6.5%
4 776
 
4.3%
5 515
 
2.9%
6 336
 
1.9%
7 294
 
1.6%
8 344
 
1.9%
9 207
 
1.2%
10 190
 
1.1%
ValueCountFrequency (%)
132 132
0.7%
112 112
0.6%
106 106
0.6%
102 102
0.6%
93 93
0.5%
87 87
0.5%
75 75
0.4%
74 74
0.4%
70 70
0.4%
68 68
0.4%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.40486
Minimum0
Maximum365
Zeros1107
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size796.5 KiB
2023-01-18T17:11:24.936331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q189
median223
Q3329
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)240

Descriptive statistics

Standard deviation124.42545
Coefficient of variation (CV)0.59991578
Kurtosis-1.3949392
Mean207.40486
Median Absolute Deviation (MAD)117
Skewness-0.22540211
Sum3722295
Variance15481.692
MonotonicityNot monotonic
2023-01-18T17:11:25.032396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1107
 
6.2%
365 946
 
5.3%
364 338
 
1.9%
264 289
 
1.6%
363 252
 
1.4%
358 233
 
1.3%
362 209
 
1.2%
356 208
 
1.2%
88 175
 
1.0%
90 164
 
0.9%
Other values (356) 14026
78.2%
ValueCountFrequency (%)
0 1107
6.2%
1 91
 
0.5%
2 39
 
0.2%
3 31
 
0.2%
4 19
 
0.1%
5 26
 
0.1%
6 18
 
0.1%
7 14
 
0.1%
8 32
 
0.2%
9 24
 
0.1%
ValueCountFrequency (%)
365 946
5.3%
364 338
 
1.9%
363 252
 
1.4%
362 209
 
1.2%
361 98
 
0.5%
360 93
 
0.5%
359 78
 
0.4%
358 233
 
1.3%
357 114
 
0.6%
356 208
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size673.8 KiB
False
10624 
True
7323 
ValueCountFrequency (%)
False 10624
59.2%
True 7323
40.8%
2023-01-18T17:11:25.134477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

accommodates
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8468268
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.5 KiB
2023-01-18T17:11:25.200525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5076226
Coefficient of variation (CV)0.52958002
Kurtosis17.750594
Mean2.8468268
Median Absolute Deviation (MAD)1
Skewness3.0260598
Sum51092
Variance2.2729258
MonotonicityNot monotonic
2023-01-18T17:11:25.269619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 8577
47.8%
4 3410
 
19.0%
3 3278
 
18.3%
1 1235
 
6.9%
5 645
 
3.6%
6 489
 
2.7%
8 86
 
0.5%
7 75
 
0.4%
10 34
 
0.2%
9 27
 
0.2%
Other values (6) 91
 
0.5%
ValueCountFrequency (%)
1 1235
 
6.9%
2 8577
47.8%
3 3278
 
18.3%
4 3410
 
19.0%
5 645
 
3.6%
6 489
 
2.7%
7 75
 
0.4%
8 86
 
0.5%
9 27
 
0.2%
10 34
 
0.2%
ValueCountFrequency (%)
16 27
 
0.2%
15 16
 
0.1%
14 19
 
0.1%
13 2
 
< 0.1%
12 13
 
0.1%
11 14
 
0.1%
10 34
 
0.2%
9 27
 
0.2%
8 86
0.5%
7 75
0.4%

Interactions

2023-01-18T17:11:21.658320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.053626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.809939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.654279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.479861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.272500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.021081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.045013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.869046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.737688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.130014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.895657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.738887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.563201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.353091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.104043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.157867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.952672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.830213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.216991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.986090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.835112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.655947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.440973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.193119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.247949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.045266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.920455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.306437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.118816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.932825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.750472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.530134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.282610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.345561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.137961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:22.011153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.395176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.213373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.021953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.837129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.616843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.510706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.439007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.227655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:22.093024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.472361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.299182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.111063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.921074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.692289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.628261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.522862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.311397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:22.179058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.558041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.386367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.202514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.006789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.774022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.745265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.611670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.397176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:22.264769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.642757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.472866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.295611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.094932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.853461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.868115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.695736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.483706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:22.358845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:15.724485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:16.562572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:17.389972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.182554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:18.938152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:19.955009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:20.780133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T17:11:21.569420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-18T17:11:25.351483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
host_idlatitudelongitudepriceminimum_nightsnumber_of_reviewscalculated_host_listings_countavailability_365accommodatesneighbourhoodroom_typeinstant_bookable
host_id1.000-0.060-0.017-0.085-0.166-0.161-0.236-0.030-0.0390.0760.0740.158
latitude-0.0601.000-0.6330.1750.0610.0380.058-0.0480.0190.6670.1160.048
longitude-0.017-0.6331.0000.004-0.0160.0500.0260.0020.0530.7150.0780.034
price-0.0850.1750.0041.000-0.026-0.0880.1260.1060.4490.1090.0610.016
minimum_nights-0.1660.061-0.016-0.0261.000-0.112-0.016-0.0770.0040.0000.0000.013
number_of_reviews-0.1610.0380.050-0.088-0.1121.0000.010-0.1660.0840.0000.0350.065
calculated_host_listings_count-0.2360.0580.0260.126-0.0160.0101.0000.0420.0480.0700.1150.184
availability_365-0.030-0.0480.0020.106-0.077-0.1660.0421.000-0.0300.0440.0800.069
accommodates-0.0390.0190.0530.4490.0040.0840.048-0.0301.0000.0550.1530.033
neighbourhood0.0760.6670.7150.1090.0000.0000.0700.0440.0551.0000.1530.080
room_type0.0740.1160.0780.0610.0000.0350.1150.0800.1530.1531.0000.031
instant_bookable0.1580.0480.0340.0160.0130.0650.1840.0690.0330.0800.0311.000

Missing values

2023-01-18T17:11:22.485446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-18T17:11:22.665658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewscalculated_host_listings_countavailability_365instant_bookableaccommodates
id
11508Amazing Luxurious Apt-Palermo Soho42762CandelaPalermo-34.58184-58.42415Entire home/apt98233301314f2
14222RELAX IN HAPPY HOUSE - PALERMO, BUENOS AIRES87710233MaríaPalermo-34.58617-58.41036Entire home/apt37287983324f2
118877Entire Studio/apt in Buenos Aires600320RoxanaPalermo-34.57734-58.43790Entire home/apt56872242174t2
14711SPECTACULAR ANCIENT HOUSE57770CintiaSan Nicolas-34.60786-58.37211Entire home/apt17370101365f3
120874Great apt 1 Bedroom - 1.5 Bath /Recoleta530261LucianoRecoleta-34.58991-58.39931Entire home/apt6514141027295f4
15074ROOM WITH RIVER SIGHT59338MonicaNuñez-34.53892-58.46599Private room43432901365f1
122907Spotless entire apt. Ideal location426566EgleAlmagro-34.61982-58.41660Entire home/apt35845298324t2
16695DUPLEX LOFT 2 - SAN TELMO64880Elbio MarianoMonserrat-34.61439-58.37611Entire home/apt72382469175t4
17451A terrace in Buenos Aires67732KarenBelgrano-34.55878-58.46476Private room492220187f2
129681Studio Rojo Design Belgrano - 2 Pax638545LuisaBelgrano-34.55601-58.45717Entire home/apt412571433f2
namehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewscalculated_host_listings_countavailability_365instant_bookableaccommodates
id
36278439Puerto Madero 2 bdr luxury apart Amenities Parking272852807Maria FernandaDique 2-34.61373-58.36363Entire home/apt139383381331t4
607042304873564249City Madero Buenos Aires Suites73536121RomuloDique 2-34.61506-58.36368Entire home/apt11291348316t3
4338028Penthouse, breathtaking views, pool22521769BerniDique 2-34.61555-58.36379Entire home/apt180942984345f6
53893506Hermoso departamento en Puerto Madero/PISCINA/GYM293790790CarlosDique 3-34.60828-58.36579Entire home/apt6928236595t2
38524423Puerto Madero Loft ★★★★★293823818MariaDique 3-34.60579-58.36555Entire home/apt97871931221t7
32279620Lumiere Place Puerto Madero408551GuilleDique 2-34.61684-58.36386Entire home/apt998837013154f2
27460266••WELCOME! New Modern Suite Puerto Madero |CasaBA183209971GiselleDique 2-34.61596-58.36367Entire home/apt202653111578t5
46217105☆PUERTO MADERO LUXURIOUS APARTMENT ☆ CasaBAires183209971GiselleDique 2-34.61459-58.36378Entire home/apt26055351581t5
31262931Puerto Madero Elegant Suite | CasaBAires183209971GiselleDique 3-34.60776-58.36477Entire home/apt202653191583t5
32204376Apartamento Puerto Madero 2 ambientes amplios!122031954MartaDique 2-34.61520-58.36415Entire home/apt11580115165t3

Duplicate rows

Most frequently occurring

namehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewscalculated_host_listings_countavailability_365instant_bookableaccommodates# duplicates
0Super located apartment in Palermo191745126MarcosPalermo-34.582890-58.427456Entire home/apt1447510018365f22
1★★★★★Boutique Studio Suite in Palermo Hollywood278440549Boutique ApartmentsPalermo-34.584511-58.437511Entire home/apt121791093139t22